Enterprise systems often require data to be uploaded into a database either through a batch process or through realtime transactions. Once the data is uploaded, it is critical to perform tests to ensure that all the data was updated successfully. Many times the testing (e.g., data validity) is performed manually. Manual testing is time consuming, error prone, and results in a large strain on the administrators of the enterprise system.
In addition, software tools are available for comparing the data of two files of the same type. For example, the data in one file may be read and compared to the data read from another file. The number of mismatches may be tabulated and displayed in a report. Such rudimentary comparison software exists and is well known to one of ordinary skill.
There also exist more sophisticated automated tools for aging a file of data. For example, aging is the process by which a program reads data from a file and advances date values in the file by a predetermined quantity of days. Techniques for aging a file are well known and were extensively used during Y2K (i.e., year 2000) testing of computer systems. Moreover, techniques are known for reading and automatically bulk converting values between different metrics (e.g., converting Centigrade to Fahrenheit) from one file to another.
There are commercially available products in the art for, inter alia, backend data testing. Some are limited in the number of database with which it can interface. A user may need to manually copy tables from other database repositories into a supported database product to compare data. In addition, there are schema limitations. Moreover, the source and target in certain products are required to be certain database tables. Regarding other products, there exist constraints on database repository volumes. Moreover, the source and destination are relational database tables with a constraint that source and target column names should be same and should be of the same data type (e.g., both should be character, if source column is character and target column is numeric; comparison can't be done.) The features disclosed herein provide benefits unavailable with the features of other products.